arXiv:2509.20086v4 Announce Type: replace Abstract: Phonemization is a critical component in text-to-speech synthesis. Traditional approaches rely on deterministic transformations and lexica, while neural methods offer potential for higher generalization on out-of-vocabulary (OOV) terms. We introduce OLaPh (Optimal Language Phonemizer), a hybrid framework that integrates extensive multilingual lexica with advanced NLP techniques and a statistical subword segmentation function. Evaluations on the WikiPron benchmark show OLaPh significantly outperforms established baselines in overall accuracy a
The continuous drive for more advanced and accessible AI models necessitates improved foundational components like phonemization for better human-computer interaction, especially in diverse linguistic contexts.
Improved phonemization enhances the realism and accuracy of text-to-speech systems, making AI more effective in applications ranging from voice assistants to educational tools and accessibility services.
The introduction of OLaPh suggests a more robust and generalized approach to language phonemization, potentially reducing the challenges of out-of-vocabulary terms and multilingual support in speech synthesis.
- · AI speech synthesis developers
- · Multilingual AI application providers
- · Accessibility technology sector
- · Consumers of voice AI
- · Legacy phonemization methods
- · Specialized linguist-driven phonemization services
Higher quality and more natural-sounding AI voices become more widely available across various languages and domains.
This improved phonemic accuracy could accelerate the adoption of AI agents and voice interfaces in diverse global markets.
Enhanced realism in synthetic speech may contribute to more sophisticated and convincing deepfakes or AI-generated media, requiring better detection measures.
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Read at arXiv cs.CL